www.atmos-chem-phys.net/15/2387/2015/ doi:10.5194/acp-15-2387-2015
© Author(s) 2015. CC Attribution 3.0 License.
Simulating aerosol–radiation–cloud feedbacks on meteorology
and air quality over eastern China under severe haze conditions
in winter
B. Zhang1,2, Y. Wang1,3,4, and J. Hao2
1Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science,
Institute for Global Change Studies, Tsinghua University, Beijing, 100084, China
2School of Environment, Tsinghua University, Beijing, 100084, China
3Department of Marine Sciences, Texas A&M University Galveston Campus, Galveston, TX 77553, USA 4Department of Atmospheric Sciences, Texas A&M University, College Station, TX 77843, USA
Correspondence to:Y. Wang (yxw@mail.tsinghua.edu.cn)
Received: 21 September 2014 – Published in Atmos. Chem. Phys. Discuss.: 17 October 2014 Revised: 18 January 2015 – Accepted: 1 February 2015 – Published: 4 March 2015
Abstract.The aerosol-radiation-cloud feedbacks on meteo-rology and air quality over eastern China under severe winter haze conditions in January 2013 are simulated using the fully coupled online Weather Research and Forecasting/Chemistry (WRF-Chem) model. Three simulation scenarios including different aerosol configurations are undertaken to distinguish the aerosol’s radiative (direct and semi-direct) and indirect effects. Simulated spatial and temporal variations of PM2.5
are generally consistent with surface observations, with a mean bias of−18.9 µg m−3(−15.0 %) averaged over 71 big cities in China. Comparisons between different scenarios re-veal that aerosol radiative effects (direct effect and semi-direct effects) result in reductions of downward shortwave flux at the surface, 2 m temperature, 10 m wind speed and planetary boundary layer (PBL) height by up to 84.0 W m−2, 3.2◦C, 0.8 m s−1, and 268 m, respectively. The simulated im-pact of the aerosol indirect effects is comparatively smaller. Through reducing the PBL height and stabilizing lower at-mosphere, the aerosol effects lead to increases in surface concentrations of primary pollutants (CO and SO2).
Sur-face O3mixing ratio is reduced by up to 6.9 ppb (parts per
billion) due to reduced incoming solar radiation and lower temperature, while the aerosol feedbacks on PM2.5 mass
concentrations show some spatial variations. Comparisons of model results with observations show that inclusion of aerosol feedbacks in the model significantly improves model performance in simulating meteorological variables and im-proves simulations of PM2.5temporal distributions over the
North China Plain, the Yangtze River delta, the Pearl River delta, and central China. Although the aerosol–radiation– cloud feedbacks on aerosol mass concentrations are subject to uncertainties, this work demonstrates the significance of aerosol–radiation–cloud feedbacks for real-time air quality forecasting under haze conditions.
1 Introduction
Models simulating the direct, indirect, and semi-direct effects of aerosols on meteorology and chemistry need to couple aerosols with physical and chemical processes. The chemistry version of the Weather Research and Forecasting (WRF-Chem) model (Grell et al., 2005) is a state-of-the-art mesoscale “online” atmospheric model, in which the chemi-cal processes and meteorology are simulated simultaneously. This design makes WRF-Chem capable of simulating aerosol feedbacks on various atmospheric processes. Several stud-ies employing WRF-Chem reveal that aerosols reduce down-ward solar radiation reaching the ground, inhibit convection, reduce the PBL (planetary boundary layer) and, hence, make the lower atmosphere more stable (Fan et al., 2008; Forkel et al., 2012; Zhang et al., 2010). WRF-Chem results also in-dicate that aerosols can modify atmospheric circulation sys-tems, resulting in changes in monsoon strength, precipita-tion distribuprecipita-tion, and midlatitude cyclones (Zhao et al., 2011, 2012).
In January 2013, several severe and long-lasting haze episodes occurred in eastern China (Fig. 1). Monthly mean mass concentrations of fine particulate matter (PM2.5)
ex-ceeded 200 µg m−3 in some cities in the North China Plain. Meteorological conditions and chemical components of PM2.5during this month have been investigated by a
num-ber of studies in order to understand the chemical characteris-tics and formation mechanism of severe winter haze episodes (Bi et al., 2014; Che et al., 2014; K. Huang, et al., 2014; Sun et al., 2014; L. T. Wang et al., 2014; Y. S. Wang et al., 2014; Y. X. Wang et al., 2014; Z. F. Wang et al., 2014; J. K. Zhang et al., 2014). Meanwhile, such high levels of PM concentra-tions are expected to exert impacts on meteorological condi-tions through the aerosol–radiation–cloud interaccondi-tions. Few of the current air quality forecasting systems for China in-clude aerosol–meteorology interactions. The significance of this effect and the extent to which it feedbacks on air quality remains to be uncertain and needs to be quantified for better future forecasting of air quality in China (Wang et al., 2013; Y. Zhang et al., 2014; Z. F. Wang et al., 2014).
In this work, the fully coupled online WRF-Chem model is employed to simulate the complex interactions between aerosols and meteorology and to characterize and quantify the influences of aerosol feedbacks on meteorology and air quality under severe winter haze conditions in January 2013 over eastern China. The aerosol direct, indirect and semi-direct effects are all included in the WRF-Chem simulation and analyzed separately. The WRF-Chem model configura-tion, scenarios setup, and observation data are described in Sect. 2. Section 3 evaluates the model performance in sim-ulating meteorology and air quality. In Sect. 4, the aerosol feedbacks on meteorology and air quality are analyzed and discussed. Section 5 investigates the effects of including aerosol feedbacks on the model performance. The conclud-ing remarks are given in Sect. 6.
Figure 1.WRF-Chem modeling domain with grid resolution of
27 km. The domain covers eastern parts of China. The triangles in-dicate the location of 523 meteorology stations used for evaluations in this work
2 Model and observations description 2.1 WRF-Chem model and scenarios setup
The WRF model is a state-of-the-art mesoscale nonhydro-static model and allows for many different choices for phys-ical parameterizations (http://www.wrf-model.org/). WRF-Chem is a chemical version of WRF that simultaneously sim-ulates meteorological and chemical components. The version 3.3 of WRF-Chem released on 6 April 2011 is used in this study. A more detailed description of the model can be found in previous studies (Grell et al., 2005; Fast et al., 2006; Chap-man et al., 2009).
The main physical options selected in this study in-clude the Goddard shortwave radiation scheme coupled with aerosol direct effects (Chou et al., 1998), the Rapid Ra-diative Transfer Model (RRTM) longwave radiation scheme (Mlawer et al., 1997), the Noah Land Surface Model (Chen and Dudhia, 2001), the Yonsei University (YSU) bound-ary layer scheme (Hong et al., 2006), the Lin microphysics scheme coupled with aerosol indirect effects (Lin et al., 1983), and the Grell–Devenyi cumulus parameterization scheme (Grell and Dévényi, 2002).
factor are calculated as a function of wavelength and three-dimensional position, and then transferred to the Goddard shortwave scheme. Mie theory is used to estimate the ex-tinction efficiency. In this study, refractive indices are cal-culated based upon a volume-averaging approximation. The first and second indirect effects are implemented in the model (Gustafson et al., 2007; Chapman et al., 2009). Activated aerosols serving as CCN are coupled with cloud micro-physics. Activation of aerosols follows the method of Ghan and Easter (2006), which is derived from Abdul-Razzak and Ghan (2002). Through this coupling, aerosols alter cloud droplet number and cloud radiative properties, while aque-ous processes and wet scavenging also affect aerosols. In the model, aerosols change vertical profiles of meteorolog-ical variables by absorbing and scattering solar radiation, and further alter cumulus parameterization. When indirect effects are included, a more comprehensive in- and below-cloud aerosol wet removal module following the method of Easter et al. (2004) is employed.
The Carbon Bond Mechanism version Z (CBMZ) (Zaveri and Peters, 1999) is used as the gas-phase chemistry scheme. The Model for Simulating Aerosol Interactions and Chem-istry (MOSAIC) (Zaveri et al., 2008) is applied as aerosol module. MOSAIC simulates aerosol species including sul-fate, methanesulfonate, nitrate, ammonium, chloride, carbon-ate, sodium, calcium, black carbon (BC), organic carbon (OC), and other unspecified inorganic matter (OIN). Sec-ondary organic aerosols (SOA) are not included in the ver-sion of MOSAIC used in this study. Section 3.3 discusses the impact of SOA on our analysis by replacing MOSAIC with the Modal Aerosol Dynamics Model for Europe (MADE) (Ackermann et al., 1998) and with the secondary organic aerosol model (SORGAM) (Schell et al., 2001) (referred to as MADE/SORGAM). MOSAIC in WRF-Chem uses a sec-tional approach to represent particle size distribution. In this study, four size bins (0.039–0.156, 0.156–0.625, 0.625–2.5, and 2.5–10.0 µm dry diameter) are employed, and aerosols are assumed to be internally mixed within each bin.
The simulated time period is the whole month of January 2013. Figure 1 illustrates the model domain, which covers eastern China (19◦–51◦N, 96◦–132◦E) and has a horizon-tal resolution of 27 km×27 km. There are 28 vertical levels extending from the surface to 50 hPa. The initial and bound-ary conditions for WRF are provided by the 6-hourly 1◦×1◦ National Centers for Environmental Prediction (NCEP) Fi-nal AFi-nalysis (FNL). Chemical boundary conditions are pro-vided by MOZART (Model for Ozone And Related chemi-cal Tracers) simulations (Emmons et al., 2010). In the initial spin-up process, the model is run with NCEP meteorological and MOZART chemical conditions for 48 h, which are suffi-cient for the meteorological and chemical variables to reach equilibrium. Considering both accuracy and efficiency, the model’s meteorology is re-initialized every 5 days based on NCEP, while chemistry adopts the previous state.
Table 1.Summary of three simulated scenarios.
Case name Characteristics
BASE With all aerosol feedbacks
RAD Only with aerosol direct and semi-direct effects EMP Without any aerosol feedbacks
In order to investigate the impact of aerosol feedbacks on meteorology and air quality, three WRF-Chem simulation scenarios are performed and compared. The first is the base-line scenario (BASE), including all aerosol effects on mete-orology (i.e., direct, indirect, and semi-direct). The second scenario (RAD) focuses on the radiative effects by excluding aerosol indirect effects from the BASE scenario. The third scenario (EMP) does not contain any aerosol effects on me-teorology. Aerosol radiative properties are connected with the shortwave radiation scheme in the BASE and RAD sce-narios but not in the EMP scenario. Cloud droplet number is prescribed to be 1.0×108particles per kg of air in EMP
and RAD, while it is calculated based on aerosol activation in BASE. Other than the differences in aerosols effects, the three scenarios are identical in input data (e.g., emissions and boundary conditions) and model setup. The difference be-tween BASE and EMP (i.e., BASE−EMP) is used to inves-tigate the impact of total aerosol feedbacks, while the differ-ence between BASE and RAD (i.e., BASE−RAD) and that between RAD and EMP (i.e., RAD−EMP) represent the in-fluence of aerosol indirect effects and radiative (both direct and semi-direct) effects, respectively. Table 1 summarizes the characteristics of the three scenarios.
2.2 Emissions
Anthropogenic emissions are taken from the Multi-resolution Emission Inventory of China (MEIC) (http://www. meicmodel.org/), which provides emissions of sulfur diox-ide (SO2), nitrogen oxides (NOx), carbon monoxide (CO), ammonia (NH3), BC, OC, PM10, PM2.5, and non-methane
volatile organic compounds (NMVOCs) for China for the year 2010. NOxemissions contain 90 % of NO2 and 10 %
of NO by mole fraction. PM emissions are assumed to be split into 20 % in nuclei mode and 80 % in accumulation mode, according to the recommended construction of anthro-pogenic emissions inventory in WRF-Chem user’s guide.
Figure 2.Time series of observed (black line) and simulated (red
line) daily meteorological variables averaged over 523 meteorology stations in January 2013.
2.3 Observations
The simulation results are compared with meteorological and chemical observations. Daily meteorological observa-tions at 523 staobserva-tions are obtained from the National Climate Data Center (NCDC) of China Meteorological Administra-tion (CMA), including 2 m temperature, 2 m relative humid-ity (RH), and 10 m wind speed. The radiosonde profiles at 20 stations are provided by the department of atmospheric science at the University of Wyoming (http://weather.uwyo. edu/upperair/sounding.html).
The measurements of near-surface PM2.5 mass
concen-trations are obtained from the China National Environmen-tal Monitoring Center (CNEMC). The observation data have been released since January 2013, including hourly concen-trations of SO2, NO2, CO, ozone (O3), PM10 and PM2.5at
74 big cities in China with 71 of those cities covered by the study domain. Each city has several observation stations, containing both urban and rural stations. In this study, the PM2.5concentration for one city is the average of all the
sta-tions in the city, representing thus a more regional condition of the pollution level. This database allows for a spatially ex-tensive evaluation of air quality simulations by atmospheric models.
3 Model evaluations
Accurate representation of meteorology and aerosol concen-trations in the model provides the foundations of quantifying
Table 2.Statistical Performance of baseline simulations for meteo-rology.
T2 RH2 WS10 PM2.5
(◦C) (%) (m s−1) (µg m−3)
No. of stations 523 523 523 71
MEAN OBS −1.8 66 1.9 129.2
MEAN SIM −2.8 66 3.9 111.5
Corr.R 0.96 0.47 0.47 0.67
MB −1.0 0 2.0 −18.9
NMB −0.4 %∗ < 0.1 % 105 % −15.0 %
RMSE 3.4 16 2.7 30.7
∗calculated in K
aerosol feedbacks. Therefore, in this section, the model per-formance is evaluated by comparing model results with sur-face and radiosonde observations. If not otherwise specified, the model results presented in this section are from the BASE scenario, which represents the most comprehensive realiza-tion of different processes in the model. It should be noted that systematic differences may result from the comparison between grid-mean values and point measurements, since a model grid covers an area of 729 km2(27 km
×27 km).
3.1 Meteorology
Meteorology strongly affects formation, transport, and elim-ination of atmospheric aerosols. The selected meteorological variables for model evaluation are temperature, relative hu-midity, and wind speed. Figure 2 shows the time series of observed and simulated daily mean 2 m temperature and 2 m relative humidity, with the statistical summary of the com-parisons shown in Table 2. The model reproduces temporal variations of these two meteorological variables.
The statistical indices used here are mean observation (MEAN OBS), mean simulation (MEAN SIM), correlation coefficient (Corr.R), mean bias (MB), normalized mean bias (NMB), and root mean square error (RMSE). The definitions of these indices are given in the Appendix. The model repro-duces 2 m temperature with a correlation of 0.97 and a cold bias of−1.0◦C, mainly due to underestimation in the first 9 days of the month. Relative humidity is simulated with a correlation of 0.47 and a negligible mean bias. The 10 m wind speed is systematically overestimated by the model by 105 %. A high positive bias in wind speed is also reported by several other studies using WRF-Chem (Matsui et al., 2009; Molders et al., 2012; Tuccella et al., 2012; Zhang et al., 2010). This high-bias probably results from unresolved topographical features in surface drag parameterization and the coarse resolution of the domain (Cheng and Steenburgh, 2005; Yahya et al., 2014).
Figure 3.Monthly mean vertical profiles of observed (black line) and simulated (red line) meteorological variables averaged over 36
meteo-rology stations.
meteorological variables are compared with sounding obser-vations (averaged at 00:00 and 12:00 UTC – universal time coordinated). Generally, the model captures vertical varia-tions of meteorological variables. The model reproduces well the vertical variations of temperature with a small bias. The model underestimates relative humidity below 650 hPa and overestimates it in the upper levels. Wind speeds are overes-timated in the lower atmosphere and underesoveres-timated in the upper atmosphere. The errors in meteorology may influence the accuracy of simulating processes of aerosol formation, transport and deposition. Overall, the evaluations presented here suggest that the model adequately simulates the spatial and temporal variations of meteorological variables of sig-nificant relevance to air quality.
3.2 PM2.5
We first evaluated the spatial distribution of simulated monthly mean PM2.5 mass concentrations by comparing
model results with observations at 71 big cities in the model domain in January 2013. As shown in Fig. 4, the model captures the spatial patterns of PM2.5during the month,
in-cluding high levels of PM2.5 over southern Hebei, Henan,
Hubei Province, Sichuan Basin and three big cities (Harbin, Changchun, Shenyang) in northeastern China. The North China Plain (NCP), central China (CC) area, and Sichuan Basin have the highest monthly mean PM2.5mass
concentra-tions. PM2.5pollution is more severe over the Yangtze River
delta (YRD) than that over the Pearl River delta (PRD). Figure 5 presents the scatter plots of observed and simu-lated monthly mean PM2.5mass concentrations at 71 cities.
The model has a low bias ranging from 25 to 70 % for cities with monthly mean PM2.5 exceeding 200 µg m−3. The
ob-served and simulated time series of hourly surface PM2.5
av-eraged over all the cities are compared in Fig. 6. The model simulates hourly PM2.5with a temporal correlation of 0.67,
and underestimates monthly mean PM2.5 mass
concentra-Figure 4.Simulated and observed (circles) monthly mean PM2.5
mass concentration over eastern China in January 2013. The four polygons correspond to the North China Plain (NCP) (1), the Yangtze River delta (YRD) (2), the Pearl River delta (PRD) (3), and central China (4).
tions by 18.9 µg m−3(15.0 %). The model generally repro-duces the observed temporal variations of PM2.5.
The enhancement ratio is employed to further evaluate the model performance in simulating PM2.5temporal variations
in different regions. Within a given grid point, the enhance-ment ratio is defined as the average of daily PM2.5mass
con-centrations exceeding the median divided by that less than the median, representing changes of PM2.5from clean to
Figure 5.Scatter plots of monthly mean PM2.5mass concentrations in 71 cities in January 2013.
Figure 6.Comparison of observed (black) and simulated (red for
the BASE scenario and blue for the EMP scenario) hourly near-surface PM2.5mass concentrations averaged over 71 cities in China
in January 2013.
simulated enhancement ratios range from 1.8 to 2.0 over the four regions, which are close to observations. Since changes of hourly emissions are not considered in this study, PM2.5
enhancements mainly result from worsened meteorological conditions and more production of secondary aerosols. The consistency of the simulated enhancement ratios with the ob-served ones by region indicates that the WRF-Chem model has some success in simulating the changes of meteorologi-cal and chemimeteorologi-cal processes from clean to polluted situations. However, the model fails to capture the extremely high values of PM2.5 during the haze episodes in January 2013,
e.g., 13–15 and 18–20 January. Both positive and negative biases exist in simulated hourly PM2.5. The model’s
under-estimation during the severe winter haze episodes is consis-tent with previous studies (Liu et al., 2010; L. T. Wang et al., 2014; Y. X. Wang et al., 2014; Zhou et al., 2014). Possible reasons for this underestimation are (1) the bias in simulat-ing meteorological conditions dursimulat-ing haze episodes; (2)
un-Table 3.Observed and simulated enhancement ratios of PM2.5. The
enhancement ratio is defined as the average of daily PM2.5larger
than the median value divided by that of hourly PM2.5 less than
the median value during the month. NCP, YRD, PRD, and CC as defined in the text.
NCP YRD PRD CC
Observations 1.7 1.6 1.5 1.7 WRF-Chem (BASE) 1.6 1.7 1.6 1.7
certainties in emissions; (3) missing SOA in the MOSAIC mechanism, which will be addressed below; and (4) the lack of formation mechanisms of secondary inorganic aerosols, like heterogeneous oxidation of SO2on the surface of
par-ticulate matter (Harris et al., 2013). By adjusting SO2 and
NOx emissions according to surface observations and pa-rameterizing the heterogeneous oxidation of SO2 on deli-quesced aerosols in the GEOS-Chem model, Y. X. Wang et al. (2014) reported improvements of simulated PM2.5
spa-tial distribution and an increase of 120 % in sulfate fraction in PM2.5. The traditional offline models do not include the
aerosol feedbacks on meteorology, which may cause a low bias of PM2.5 for the severe pollution episodes. The
inclu-sion of aerosol feedbacks in atmospheric models improves the model performance, which will be discussed in Sect. 5.
3.3 SOA
The MOSAIC aerosol module used in the three scenarios does not include SOA. Here we examine the sensitivity of modeled PM2.5 to SOA by replacing MOSAIC with the
emis-Table 4.Modeled SOA, OA and PM2.5using MADE/SORGAM
and MOSAIC in comparison with observations. The data shown are spatially averaged over 71 big cities and temporally averaged during 6–10 January 2013.
SOA OA SOA/OA PM2.5 NMB of
(µg m−3) (µg m−3) (µg m−3) PM2.5 MADE/
SORGAM 0.3 9.4 2.8 % 68.3 49.7 %
MOSAIC 115.4 15.0 %
Observations 135.7
sions of biogenic VOCs (key precursors of SOA) and low temperatures in wintertime. Recent observations have sug-gested much higher SOA concentrations at Chinese cities in winter, pointing to the importance of anthropogenic VOCs as SOA precursors in China (R. J. Huang et al., 2014); however, a thorough investigation of this issue is outside the scope of this study. As shown in Table 4, the NMB of PM2.5with the
MADE/SORGAM option is more than 3 times larger than that with MOSAIC. Since the modeled SOA has a small con-tribution to total PM2.5, the MOSAIC aerosol module is
cho-sen in this study and the omission of SOA in MOSAIC is not expected to affect our analysis of the aerosol effects on meteorology.
4 Aerosol feedbacks on meteorology and air quality As seen in the previous section, the WRF-Chem model has shown some success in simulating meteorology and PM2.5.
Therefore, in this section, we aim to characterize and quan-tify the aerosol feedbacks on meteorology and air quality by comparing the three different scenarios described in Sect. 2.
In addition to different setups of the aerosol–radiation– cloud feedbacks, differences among the three scenarios can also result from model noise, such as errors in numerical computation and disturbances from discrete updating of ini-tial and boundary conditions. The Studentt test is employed to identify statistically significant differences between the scenarios. The null hypothesis is that the two scenarios in comparison give the same simulation results. The sample size is 744 (24 h×31 days) for each meteorological and chemical variable in a given grid box. Rejection of the null hypothe-sis indicates that the difference between the two scenarios is significantly different. The Appendix describes the calcula-tion of thet statistic in more detail. We only present and dis-cuss aerosol-induced changes of meteorological and chemi-cal variables which exceed a 95 % confidence interval.
4.1 Feedbacks on meteorology
The evolution of atmospheric aerosols is strongly influenced by meteorological variables such as solar radiation, air tem-perature, and wind speed. Figure 7 illustrates the mean
pact of aerosols on downward shortwave flux at the ground, 2 m temperature, 10 m wind speed and PBL height over eastern China in January 2013. Downward shortwave flux at the ground is strongly influenced by the existence of atmospheric aerosols, especially over high aerosol-loading regions. Aerosols affect shortwave radiation reaching the ground in two ways. First, particles scatter and absorb in-coming solar radiation directly, resulting in surface dimming. Second, in-cloud particles change cloud lifetime and albedo, thus, causing variations of shortwave radiation at the ground surface. As in Fig. 7a, the downward shortwave flux at the ground is reduced over the vast areas of eastern China by up to−84.0 W m−2, which mainly results from the aerosol ra-diative effects (Fig. 7b). Consistent with our findings, Bi et al. (2014) and Che et al. (2014) report strong negative aerosol direct radiative forcing at the surface (with maximum daily mean exceeding−200.0 W m−2) through analysis of ground-based measurements, aerosol optical and radiative properties over the NCP in January 2013.
When the downward shortwave flux at the ground is decreased due to aerosol interception, near-surface energy fluxes are suppressed, leading to weaker convection. Near-surface air is heated mainly by longwave radiation emitted from the ground. In the case of decreasing shortwave flux at the ground, the near-surface air is cooled and less long-wave radiation is emitted from the surface. Due to weaker convection resulting from less shortwave radiation reaching the ground, 2 m temperature is reduced by up to 3.2◦C, 10 m wind speed is reduced by up to 0.8 m s−1, and PBL height is also reduced by up to 268 m, as shown in Fig. 7c, e and g, respectively. Meteorological variables such as air temper-ature, wind speed, and PBL height could also be influenced by other factors like land surface properties (Zhang et al., 2010), in addition to solar radiations. The change of these variables, especially wind speed, is less significant than that of solar radiation. First, solar radiation is reduced by 21 % over the regions with significant changes, while wind speed and PBL height are reduced by 6 and 14 %, respectively. Sec-ond, as shown in Fig. 7, the regions with significantly re-duced solar radiation are much larger than those with sig-nificantly reduced wind speed or PBL height. However, the spatial pattern of the changes of these variables is consistent with that of downward shortwave flux, which indicates that a more stable lower atmosphere resulting from less shortwave radiation plays an important role in aerosol feedbacks. The aerosol indirect effects during the severe haze episodes are found to be not significant in altering solar radiation, temper-ature, wind speed or PBL height over eastern China, which is not shown here. Overall, the near-surface atmosphere is more stable when aerosol feedback is considered in the model, which enhances pollution accumulation.
The amount of precipitation is low in January 2013 for most regions in China (Wang and Zhou, 2005). Cloud and precipitation formation mainly occurs over areas in the south and over the ocean (Fig. 8a, d). In this month, the changes
of cloud and precipitation due to aerosol radiative effects are not significant (Fig. 8h). Aerosol indirect effects directly al-ter cloud properties such as effective radius, cloud lifetime, and precipitation rate. As shown in Fig. 8i, aerosol indirect effects play a much more significant role in changing cloud properties, mostly in the south. Cloud water path is greatly reduced by up to 5.7 kg m−2over the junction of Yunnan and the Guizhou Province and the ocean around Taiwan. The re-duction over these relatively clean areas may be explained by the smaller droplet number mixing ratio which is derived from lower particle number concentrations in the BASE sce-nario. The scenarios without aerosol indirect effects adopt the default value droplet number mixing ratio of 1.0×108 parti-cles per kg of air which does not vary with aerosol num-ber concentrations. The reduced cloud droplet numnum-ber results in accelerating auto-conversion to rain droplets. Thus, sim-ulated monthly precipitation is increased by almost 100 % over these areas (Fig. 8j). Similar results are found when we replace the Lin microphysics scheme by the two-moment Morrison scheme (Morrison et al., 2009). Previous model as-sessments also showed that the inclusion of aerosol indirect effects reduced cloud water content over the South Pacific Ocean and improved model comparison with aircraft obser-vations (Yang et al., 2011). The relatively small precipitation, as well as small changes of precipitation due to aerosol feed-backs over the north of the domain, suggests that precipita-tion has a minor effect on near-surface aerosols in January 2013.
Several studies under the Air Quality Model Evaluation International Initiative (AQMEII) project suggested that the aerosol indirect effect dominated in aerosol feedbacks on solar radiation, temperature, and PBL height (Forkel et al., 2012, 2014; Kong et al., 2014; Makar et al., 2014a). Differ-ent from these studies, we find that aerosol indirect effects have little influence on the downward shortwave flux at the ground, 2 m temperature, 10 m wind speed, and PBL height (not shown here). In order to investigate how grid resolution relates to the aerosol indirect effect, we have conducted an-other three groups of nested grid runs, using the same scenar-ios (BASE, RAD, and EMP). The outer domain is the same as the previous domain, but a 9 km nested domain covering the North China Plain is added. In the nested domain, cumu-lus parameterization is turned off. We find that with the finer-resolution simulation, the aerosol direct effect still dominates over the NCP and the aerosol indirect effect is not significant, suggesting grid resolution might not be the reason why the aerosol indirect effect is minor in our study.
so-Figure 8.Simulated cloud water path(a–c)and precipitation(d–f)for the three scenarios and aerosol total effects (BASE−EMP) (g,j),
Figure 9.Simulated monthly mean CO, SO2, and O3mixing ratios and aerosol feedbacks (BASE−EMP) on the three gas pollutants over eastern China in January 2013. Grey shaded areas indicate regions with less than 95 % significance.
lar radiation, temperature, and PBL height were found to be most pronounced (Forkel et al., 2014). Second, under high aerosol loading conditions (for example, fire conditions in Kong et al. (2014) and haze conditions in our study), the aerosol direct effect is found to dominate over the aerosol indirect effect. The aerosol indirect effect was found to dom-inate over clean ocean and near the ocean’s coast (Forkel et al., 2014; Kong et al., 2014). Third, we do find some regions dominated by the aerosol indirect effect. However, the Stu-dentt test filters the values over those regions because the changes due to the aerosol indirect effect are not statistically significant.
Ice crystals can be formed by activation of ice nuclei (IN). Recently, increasing evidence has suggested that aerosols, es-pecially dust, black carbon, and organic matter, can influence cloud physical process by acting as IN (Tao et al., 2012). The WRF-Chem version in this work does not include direct in-teractions of aerosol number concentration with ice nuclei, which may be another reason for the possible underestima-tion of aerosol indirect effects.
4.2 Feedbacks on air quality
Through moderating meteorological variables, aerosols ex-ert feedbacks on air quality. Figure 9 shows spatial distribu-tions of CO, SO2, and O3and the feedbacks of aerosols on
these three gas pollutants in January 2013. Spatial patterns of CO and SO2are similar to that of PM2.5. The near-surface
CO and SO2concentrations are increased when aerosol
feed-backs are included. Over the domain, CO is enhanced by up to 446 ppb, while SO2 is increased by as much as 28 ppb.
Large increases of CO and SO2are found over areas with
high aerosol loading. This phenomenon may mainly result from a lower PBL and a more stable atmosphere near the sur-face due to aerosol radiative effects, as discussed in Sect. 3.2. We also found aerosol indirect effects do not have significant influence on gas pollutants.
The formation of O3is directly related to solar radiation and temperature in regions with sufficient NOx and VOCs. The lower air temperature and reduced incoming solar radi-ation as a result of aerosol radiative effects lead to a reduced photolysis rate of NO2and consequently reduce O3
Changes in northern China are relatively small. These find-ings are similar to those in Zhang et al. (2010), Forkel et al. (2012), Kong et al. (2014) and Makar et al. (2014b).
The aerosol–radiation–cloud feedbacks on near-surface aerosol mass concentrations are illustrated in Fig. 10. As shown in Fig. 10a, both increases and decreases of PM2.5 are found in the domain. Enhanced PM2.5 mass
concentrations are simulated over Henan, Hubei, Guangxi Province, and the Sichuan Basin with the maximum enhance-ment of 69.3 µg m−3. Reductions in PM2
.5 by as much as
−38.2 µg m−3are simulated over the Bohai Sea’s
surround-ing area, northeastern China and the conjunction area of Yunnan, Guangxi, and the Guizhou Province (southwestern China).
In order to better understand the mechanisms of how PM2.5responds to aerosol feedbacks, the aerosol effects are
divided into aerosol radiative effects (Fig. 10b) and indirect effects (Fig. 10c). PM2.5 can be influenced by the changes
in various atmospheric processes due to aerosol radiative ef-fects. For example, lower temperature may suppress the for-mation of sulfate, and reduced solar radiation may inhibit the oxidation of precursors of secondary aerosols. Among the various changes in atmospheric processes, the reduced PBL height and the stabilized lower atmosphere may be the most important factors to explain the increase of PM2.5 caused
by the aerosol radiative effects, since primary gas pollutants also increase when aerosol radiative effects are included. In Fig. 11b, we can see that PM2.5 is greatly increased by
aerosol radiative effects over the region where solar radia-tion and PBL height are significantly reduced during winter haze (Fig. 7a, g). The mechanism involved is that aerosol ra-diative effects stabilize the lower atmosphere and suppress the dilution and ventilation of PM2.5as well as primary gas
pollutants.
The comparison between Fig. 10a and c indicates that aerosol indirect effects are the main reason of the suppres-sion of PM2.5. The reduction of PM2.5in WRF-Chem
simu-lations with aerosol indirect effects mainly comes from three aspects. First, once aerosol indirect effects are included in the model, the cloud droplet number is based on a simu-lated atmospheric aerosol number different than what is pre-scribed in the model. This coding strategy allows intersti-tial air-borne aerosols to become cloud-borne aerosols af-ter activation. Therefore, air-borne aerosols are reduced in simulations including aerosol indirect effects, especially over cloudy regions like southwestern China (Fig. 8a). Second, enhanced precipitation may also help reduce the air-borne particles, especially in the south. Third, in the simulations including aerosol indirect effects, a more comprehensive in-and below-cloud aerosol wet removal module following the method of Easter et al. (2004) is employed, while in the simu-lations without aerosol indirect effects, this module is not ac-tivated. In this aerosol wet removal mechanism, the removal processes are assumed to be irreversible, and aerosol resus-pension is not considered even when precipitation is weak.
This leads to a stronger removal of atmospheric aerosols when aerosol indirect effects are included. It should be noted that in this work, the enhancement of aerosol wet removal process, when including aerosol indirect effects in the model, mainly results from WRF-Chem model configurations used here, not from aerosol-induced changes in cloud properties or precipitation.
The above discussion is based on model results tempo-rally averaged during the whole month. In order to better un-derstand PM2.5variations on a day to day basis, four cities
with significant PM2.5 enhancements and four cities with
significant PM2.5 reductions are selected. Figure 11 shows
the time series of observed and simulated hourly surface PM2.5mass concentrations in the selected eight cities in
Jan-uary 2013. The four cities with increasing monthly mean PM2.5 due to aerosol feedbacks are Zhengzhou, Wuhan,
Changsha, and Chengdu (left panels in Fig. 11). Major en-hancements of PM2.5 are simulated when PM2.5 levels are
high, for example, during the period of 15–17 January. The changes in PM2.5have a moderate negative correlation with
the changes in PBL height (correlations coefficient≈ −0.3) at the four cities, suggesting that the PM2.5enhancement is
partly caused by decreased PBL height in these regions. The reduction of PM2.5in Qinhuangdao, Tianjin, Shenyang, and
Changchun (right panels in Fig. 11), which are the four cities with decreased monthly mean PM2.5, mainly happens in the
last 5 days of the month (26–31 January).
In summary, aerosol radiative effects reduce the downward shortwave flux at the ground, decrease near-surface temper-ature and wind speed, and further weaken convection, all leading to a more stable lower atmosphere. In a more stable lower atmosphere due to aerosol radiative effects, primary gas pollutants (CO and SO2) and PM2.5are enhanced, while
O3is decreased because of less incoming solar radiation and lower temperatures. PM2.5is reduced when aerosol indirect
effects are included, mainly due to the transition from air-borne aerosol to cloud-air-borne aerosol and the activation of a more comprehensive aerosol wet removal module. The un-derestimations at the higher end indicate that some key mech-anisms are missing in the model, especially the production of secondary aerosols.
5 Effects of including aerosol feedbacks on model performance
In this section we address the question of whether includ-ing aerosol feedbacks within the model improves model per-formance in simulating severe haze episodes. Model results from the BASE (with all aerosol feedbacks) and EMP (with-out any aerosol feedbacks) scenarios are compared with ob-servations to evaluate which scenario is more consistent with reality.
Figure 10.Simulated aerosol total effects (BASE−EMP), radiative effects (RAD−EMP), and indirect effects (BASE−RAD) on monthly mean PM2.5over eastern China in January 2013. Grey shaded areas indicate regions with less than 95 % significance. The black polygon
defines the Bohai Sea’s surrounding area.
Figure 11.Time series of observed (black) and simulated (red for BASE scenario and blue for EMP scenario) hourly surface PM2.5mass
concentrations in eight cities for January 2013. Monthly mean PM2.5is enhanced in the four cities (Zhengzhou, Wuhan, Changsha, and
Chengdu) in the left column. Cities in the right column have suppressed monthly mean PM2.5mass concentrations. “Diff” indicates aerosol
Figure 12.Time series of observed (black) and simulated (red for the BASE scenario, purple for the RAD scenario, and blue for the EMP scenario) daily total radiation and 2 m temperature averaged over northern and central China in January 2013.
feedbacks, Fig. 12 compares downward shortwave radiation at the ground and 2 m temperature among the three sce-narios over NCP, where PM2.5 pollution is most severe in
January 2013. All scenarios have a high bias in daily total shortwave radiation at the ground, mainly due to the over-estimation of maximum shortwave radiation at noon (Z. F. Wang et al., 2014). However, the inclusion of all aerosol feedbacks (BASE) leads to a 22 % reduction of the normal-ized mean bias. Simulated shortwave radiation in the RAD scenario has the smallest bias. The model prediction of 2 m temperature is also improved in the scenario with aerosol feedbacks during haze episodes, such as on 12–15 and 19– 24 January. These findings are consistent with the those from Z. F. Wang et al. (2014), indicating the importance of includ-ing aerosol feedbacks in simulatinclud-ing meteorology under high aerosol loading conditions.
Figures 5 and 6 compare simulated PM2.5 over 71 big
cities in January 2013 in the BASE and EMP scenarios aver-aged temporally and spatially, respectively. However, no sig-nificant improvements are found when aerosol feedbacks are included, partially due to temporal and spatial averaging. So we further investigate the model performance in simulating PM2.5over several important regions. Box plots of monthly
mean PM2.5mass concentrations in January 2013 over the
NCP, YRD, PRD and CC are displayed in Fig. 13. Over all four regions, the median values of hourly PM2.5are
under-estimated in the EMP scenario, in which aerosol feedbacks are excluded. Biases of the median values in the EMP
sce-Figure 13.Observed (black) and simulated (red for the BASE
sce-nario and blue for the EMP scesce-nario) monthly mean PM2.5mass
concentrations in January 2013 over the NCP, YRD, PRD and CC. The dashed lines indicate the maximum and minimum values. The solid lines in the box indicate the median value (the central line), the 25th and 75th percentiles.
nario are−29.1,−16.8,−10.7, and−5.3 % over NCP, YRD, PRD, and CC, respectively. By including aerosol feedbacks, the BASE scenario improves the simulation of hourly PM2.5
mass concentrations in two aspects. First, biases of the me-dian values are reduced to−22.0,−12.0,−6.7, and+2.6 % over NCP, YRD, PRD, and CC, respectively. Second, the dis-tribution of the middle 50 % (ranging from 25th to 75th per-centile) hourly PM2.5 mass concentrations is more
consis-tent with observations than without aerosol feedbacks in the model. We also find a positive feedback for PM2.5; that is,
aerosols increase PM2.5through meteorological and
chemi-cal processes.
Overall, in this section we demonstrate the significance of including aerosol feedbacks in the model. Inclusions of aerosol feedbacks in the model reproduce aerosol effects on solar radiation and temperature. Thus, biases of simulated meteorology are reduced. Though the responses of PM2.5
to aerosol feedbacks are complex, the inclusion of aerosol feedbacks improves the model performance to some extent in simulating PM2.5in winter haze conditions.
6 Conclusions
meteorology and air quality over eastern China in January 2013, the month in which China experienced the most severe haze pollution in history. Three simulation scenarios includ-ing different aerosol configurations are undertaken and com-pared.
The evaluation of the baseline simulation shows that the model captures temporal and vertical variations of meteo-rological variables, except for overestimating lower atmo-sphere wind speed which is a common issue for the WRF-Chem model. The model reproduces spatial distribution of monthly mean PM2.5mass concentration, with high aerosol
concentrations over southern Hebei, Henan, Hubei Province, Sichuan Basin and three big cities (Harbin, Changchun, and Shenyang) in northeastern China. Monthly mean PM2.5
av-eraged over 71 big cities is underestimated by 15 %. The model tends to underestimate PM2.5at the high ends, which
is a common problem for current models in simulating se-vere haze conditions. Further studies are needed to improve model abilities of simulating high aerosol pollution.
Previous work indicated that the influences on air qual-ity meteorology of aerosol indirect effects are larger than ra-diative effects, but this was derived under conditions with much lower aerosol loadings than those in our study. In this work we find that under winter haze conditions, aerosol ra-diative effects (direct effect and semi-direct effects) play a dominant role in modulating downward shortwave flux at the ground surface, lower atmosphere temperature, wind speed, and PBL height. These four meteorological variables are re-duced by up to 84.0 W m−2, 3.2◦C, 0.8 m s−1, and 268 m, respectively. However, aerosol indirect effects are more im-portant than radiative effects in altering cloud properties and precipitation.
The lower PBL and stabilized lower atmosphere result in increases of near-surface CO and SO2concentrations. Higher aerosol loading reduces solar radiation and temperature at the surface, which results in a reduction of NO2photolysis rate and subsequently a reduction of O3 mixing ratios by up to
6.9 ppb. The aerosol feedbacks on PM2.5concentrations
ex-hibit large spatial variations. Both increases and decreases of PM2.5are found in the domain. The enhancements of PM2.5
over Henan, Hubei Province, and the Sichuan Basin by up to 17.8 µg m−3are mainly due to a large reduction of PBL height in these areas. The reduction of PM2.5over the Bohai
Sea’s surrounding area, northeastern China, and southwest-ern China result from the transition from air-borne aerosol to cloud-borne aerosol and the activation of a more comprehen-sive aerosol wet removal module.
The inclusion of aerosol feedback improves the model’s ability in simulating downward shortwave radiation and tem-perature. Simulations of hourly PM2.5 mass concentration
distributions over the NCP, YRD, PRD, and CC, are also im-proved when aerosol feedbacks are included. These indicate the importance of involving aerosol–radiation–cloud interac-tions in modeling air quality meteorology.
Appendix A
The statistical indices used in this study are defined as fol-lows.
1. Mean bias (MB):
MB=1/N
N
X
i=1
(Mi−Oi).
2. Normalized mean bias (NMB):
NMB=
N
P
i=1
(Mi−Oi) N
P
i=1 Oi
·100%.
3. Correlation coefficient (Corr.R):
Corr. R=
N
P
i=1
(Mi−M)(Oi−O)
s
N
P
i=1
(Mi−M)2
s
N
P
i=1
(Oi−O)2
.
4. Root mean square error (RMSE):
RMSE=
v u u
t1/N
N
X
i=1
(Mi−Oi)2,
whereM=1/N
N
P
i=1
Mi, O=1/N N
P
i=1
Oi, andMandO
are model result and observation for samplei, respec-tively.Nis the number of samples.
5. The Studenttstatistic:
t= X1−X2 SX1X2
√1/n
1+1/n2,
SX1X2= s
(n1−1)SX21+(n2−1)S
2
X2
n1+n2−2 .
Here,SX1X2 is the grand standard deviation, SX21 and
S2X
Acknowledgements. This research was supported by the National Key Basic Research Program of China (2014CB441302), the CAS Strategic Priority Research Program (grant no. XDA05100403), and the Beijing Nova Program (Z121109002512052).
Edited by: X.-Y. Zhang
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